综合考虑植被现状，利用遥感反演模拟区域土壤水分分布，基于植被耗水模型，模拟不同区域土壤水分植被承载力。
\n首先以干旱指数、土壤质地、植被类型、坡度、坡向等因子为主要划分依据，将研究区划分成了28987个植被响应单元（VRU）。其次，利用数理统计的方法将NOAA-AVHRR数据集进行降尺度处理，对1982-2006年NOAA-AVHRR 16天最大合成NDVI数据集和2000-2017年MODIS-MOD13A2 16天最大合成NDVI数据集进行融合，获得1982-2017年空间分辨率为1km的年值NDVI数据集。并利用Mann-Kendall（MK）趋势检验分析方法，对1982-2017年植被数据集进行趋势检验，获得研究区植被无明显变化的区域，作为植被稳定区。利用VRU与稳定的自然植被区域相嵌套，得到14431个VRU，包括1720个VRU类别。
\n利用实测数据对Bridge Event And Continuous Hydrological（BEACH）土壤水分模型的参数进行进一步率定，并对模型进行改进，使之能够同时模拟四层（0-10、10-40、40-100和100-200 cm）土壤水分含量。
\n以上述土壤水分模型模拟获得的四层土壤水分为自变量，以植被盖度为因变量，对各植被响应单元内做多元线性回归分析。共获得1720个多元线性拟合函数。
\n根据研究获得的土壤水分与植被盖度的拟合函数，以近10年（2008-2017年）土壤水分为自变量，计算该时段内的植被盖度，得到在该时段内土壤水分所能承载的植被盖度，作为土壤水分植被承载力。分别提取每个象元的最大值、最小值与平均值，代表自然条件下，该时段内土壤水分所能承载的植被盖度的上限、下限以及当前植被盖度。", "ds_source": "
综合考虑植被现状，利用遥感反演模拟区域土壤水分分布，基于植被耗水模型，模拟不同区域土壤水分植被承载力。", "ds_process_way": "
利用数理统计的方法将NOAA-AVHRR数据集进行降尺度处理，对1982-2006年NOAA-AVHRR 16天最大合成NDVI数据集和2000-2017年MODIS-MOD13A2 16天最大合成NDVI数据集进行融合，获得1982-2017年空间分辨率为1km的年值NDVI数据集。并利用Mann-Kendall（MK）趋势检验分析方法，对1982-2017年植被数据集进行趋势检验，获得研究区植被无明显变化的区域，作为植被稳定区。利用VRU与稳定的自然植被区域相嵌套，得到14431个VRU，包括1720个VRU类别。利用遥感反演模拟区域土壤水分分布，基于植被耗水模型，模拟不同区域土壤水分植被承载力。\n\n
\n根据研究获得的土壤水分与植被盖度的拟合函数，以近10年（2008-2017年）土壤水分为自变量，计算该时段内的植被盖度，得到在该时段内土壤水分所能承载的植被盖度，作为土壤水分植被承载力。分别提取每个象元的最大值、最小值与平均值，代表自然条件下，该时段内土壤水分所能承载的植被盖度的上限、下限以及当前植被盖度。", "ds_quality": "
数据质量良好", "ds_acq_start_time": "2020-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "北方半干旱区", "ds_acq_lon_east": 107.0, "ds_acq_lat_south": 35.5, "ds_acq_lon_west": 104.0, "ds_acq_lat_north": 50.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 20756581, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "1000", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "035525ca-f58d-4314-872c-2bfedccb2674.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "8534e8f7-cbd5-4771-81d6-d524ffde0065", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.ZDYF.db1685.2022", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-04-18 11:33:37", "last_updated": "2022-04-24 17:13:29", "protected": false, "protected_to": "2024-01-13 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.ZDYF.db1685.2022", "i18n": { "en": { "title": "Distribution map of soil moisture and vegetation carrying capacity in semi-arid areas of northern China (2020)", "ds_format": "", "ds_source": "
<p> Considering the current situation of vegetation, the regional soil moisture distribution is simulated by remote sensing inversion, and the vegetation carrying capacity of soil moisture in different regions is simulated based on the vegetation water consumption model\n <p> Good data quality\n\nConsidering the current situation of vegetation, the regional soil moisture distribution is simulated by remote sensing inversion, and the vegetation carrying capacity of soil moisture in different regions is simulated based on the vegetation water consumption model.
\nFirstly, the study area is divided into 28987 vegetation response units (VRU) based on drought index, soil texture, vegetation type, slope and aspect. Secondly, the NOAA-AVHRR data set is downscaled by using the method of mathematical statistics. The 16 day maximum synthetic NDVI data set of NOAA-AVHRR from 1982 to 2006 and the 16 day maximum synthetic NDVI data set of modis-mod13a2 from 2000 to 2017 are fused to obtain the annual NDVI data set with spatial resolution of 1km from 1982 to 2017. The Mann Kendall (MK) trend test analysis method is used to test the trend of the vegetation data set from 1982 to 2017, and the area with no obvious change in vegetation in the study area is obtained as the vegetation stability area. By nesting vrus with stable natural vegetation areas, 14431 vrus are obtained, including 1720 VRU categories.
\nThe parameters of bridge event and continuous hydrological (Beach) soil moisture model are further calibrated by using the measured data, and the model is improved to simulate the soil moisture content of four layers (0-10, 10-40, 40-100 and 100-200 cm) at the same time.
\nTaking the four layers of soil moisture simulated by the above soil moisture model as the independent variable and the vegetation coverage as the dependent variable, multiple linear regression analysis was carried out in each vegetation response unit. A total of 1720 multivariate linear fitting functions were obtained.
\nAccording to the fitting function between soil moisture and vegetation coverage obtained in the study, taking the soil moisture in recent 10 years (2008-2017) as the independent variable, calculate the vegetation coverage in this period, and obtain the vegetation coverage that the soil moisture can carry in this period as the soil moisture vegetation carrying capacity. The maximum, minimum and average values of each pixel are extracted respectively to represent the upper and lower limits of vegetation coverage that soil moisture can carry in this period and the current vegetation coverage under natural conditions.
", "ds_time_res": "", "ds_acq_place": "Northern semi-arid region", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "\nThe NOAA-AVHRR data set is downscaled by using the method of mathematical statistics. The 16 day maximum synthetic NDVI data set of NOAA-AVHRR from 1982 to 2006 and the 16 day maximum synthetic NDVI data set of modis-mod13a2 from 2000 to 2017 are fused to obtain the annual NDVI data set with spatial resolution of 1km from 1982 to 2017. The Mann Kendall (MK) trend test analysis method is used to test the trend of the vegetation data set from 1982 to 2017, and the area with no obvious change in vegetation in the study area is obtained as the vegetation stability area. By nesting vrus with stable natural vegetation areas, 14431 vrus are obtained, including 1720 VRU categories. The regional soil moisture distribution is simulated by remote sensing inversion, and the vegetation carrying capacity of soil moisture in different regions is simulated based on the vegetation water consumption model.\n
\nAccording to the fitting function of soil moisture and vegetation coverage obtained in the study, taking the soil moisture in recent 10 years (2008-2017) as the independent variable, calculate the vegetation coverage in this period, and obtain the vegetation coverage that the soil moisture can carry in this period as the soil moisture vegetation carrying capacity. The maximum value, minimum value and average value of each pixel are extracted respectively to represent the upper limit, lower limit and current vegetation coverage that soil moisture can carry in this period under natural conditions.", "ds_ref_instruction": " When users use data, please clearly state the source of data in the body and quote the reference method provided by this metadata in the References section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "土壤水分", "植被", "水分植被承载力" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北方半干旱区" ], "ds_time_tags": [ 2020 ], "ds_contributors": [ { "true_name": "杜鹤强", "email": "dilikexue119@163.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "" }, { "true_name": "刘树林", "email": "liusl@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "廖杰", "email": "liaojie@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "杜鹤强", "email": "dilikexue119@163.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "" } ], "ds_managers": [ { "true_name": "敏玉芳", "email": "myf@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "沙漠与荒漠化" }